Mesh Saliency via Weakly Supervised Classification-for-Saliency CNN

Ran Song, Yonghuai Liu, Paul Rosin
2019 IEEE Transactions on Visualization and Computer Graphics  
Recently, effort has been made to apply deep learning to the detection of mesh saliency. However, one major barrier is to collect a large amount of vertex-level annotation as saliency ground truth for training the neural networks. Quite a few pilot studies showed that this task is difficult. In this work, we solve this problem by developing a novel network trained in a weakly supervised manner. The training is end-to-end and does not require any saliency ground truth but only the class
more » ... p of meshes. Our Classification-for-Saliency CNN (CfS-CNN) employs a multi-view setup and contains a newly designed two-channel structure which integrates view-based features of both classification and saliency. It essentially transfers knowledge from 3D object classification to mesh saliency. Our approach significantly outperforms the existing state-of-the-art methods according to extensive experimental results. Also, the CfS-CNN can be directly used for scene saliency. We showcase two novel applications based on scene saliency to demonstrate its utility.
doi:10.1109/tvcg.2019.2928794 pmid:31329121 fatcat:qacg2u5o7vgaflf2iurexdybna